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Design and experimental research of wheeled inspection robot for main coal flow transportation roadway based on rocker walking mechanism
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<Abstract>(8)
Abstract:
At present, the main coal flow transportation system of most coal mines in China has basically realized continuity, mechanization and automation, which puts forward higher requirements for the safety monitoring and inspection efficiency in the main transportation roadway, and the research and development and application demand of safety control roadway inspection robots are thriving. At present, the roadway inspection robot mainly adopts the suspension track inspection mode, but due to the problem of viewing angle, it cannot inspect the equipment with a low position and is blocked, and it is difficult to meet the all-round inspection needs of underground roadway and equipment. Therefore, it is urgent to develop a more flexible and mobile wheeled inspection robot, which can be used in conjunction with the orbital inspection robot to realize the all-round inspection of the underground main roadway and its internal equipment. In this paper, the system structure of the inspection robot is determined through the analysis of the underground roadway inspection scene, and the obstacle crossing walking mechanism of the robot is analyzed and designed. In order to meet the walking performance requirements of wheeled robots under special terrain conditions of roadways, the quantitative models of crawler, wheeled crawler and rocker walking systems were established, and the performance of the robot walking system was comprehensively analyzed by using Delphi method and network analysis method, and the results showed that the robot mobile chassis based on the rocker walking mechanism had the best walking adaptability in the underground roadway environment. Finally, facing the topographical characteristics of the main roadway and the transportation equipment environment of the underground coal mine, the underground roadway model was built, and the robot inspection test in the simulated roadway was carried out in the laboratory, and the results showed that the rocker wheeled inspection robot showed good environmental adaptability in the walking test of ramps, steps and ditches, and could meet the inspection needs of the underground roadway and its main equipment, so as to provide theoretical and technical support for the realization of all-round robot inspection of the underground main roadway.
Research on Resistance Verification and Instability Criterion of Flexible Formwork Concrete Wall Support for Leaving Tunnels Along the Gob
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<Abstract>(21)
Abstract:
The stability of the overburden rock caving structure and concrete wall in gob-side entry retaining using a flexible formwork concrete wall is crucial for its success. This study focuses on the 52606 flexible formwork concrete wall in the Daliuta Coal Mine. By combining physical simulation and theoretical analysis, we examined the caving structure of overburden rock during the mining process and calculated the stability of the surrounding rock in the retaining roadway. The support resistance of the concrete wall in different stages determines its safety factor. Results from the simulation experiment reveal that after mining the two working faces, the collapse of the overlying strata above the concrete wall forms a short cantilever beam structure, with a larger collapse angle on the concrete wall side compared to the coal wall side. The first fracture position of the roadway roof along the goaf is on the side of the concrete wall above the goaf, while the second fracture position is in the upper overburden rock forming the cantilever beam structure. The mechanical parameters needed to calculate the concrete wall's support resistance were obtained. The stability of the concrete wall during different mining stages is related to the ratio N of the ultimate load to the actual load. When N > 1, the concrete wall remains stable during the mining process. This study provides a significant theoretical basis for determining instability and controlling the roof in gob-side entry retaining with a flexible formwork concrete wall in China.
Investigations on the disaster mechanism and roof cutting control technology of overhanging thick-hard roof
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<Abstract>(18)
Abstract:
In response to the problem of large deformation and high instability risk of surrounding rock in the gob-roadway under the lateral thick-hard overhanging roof, this article takes the headgate of the No. 3810 longwall face of Majiliang Coal Mine as the engineering background and investigates the field deformation and failure characteristics. A mechanical model for thick and hard lateral suspended roof was established to determine the reasonable cutting position, which is the distance of coal pillars within 3.98m. A UDEC numerical calculation model was established to invert and analyze the influence of the length and cutting position of the thick- hard overhanging roof on the vertical stress distribution, failure depth, and deformation and failure characteristics of the coal pillar and surrounding rock. The disaster caused by the thick - hard lateral overhanging roof and the pressure relief control mechanism of the roof cutting were revealed. Based on theoretical analysis and simulation results, a hydraulic fracturing for roof cutting technology scheme and its key parameters were proposed and successfully applied in on-site engineering practice. The results show that the maximum deformation of the two sides of the roadway is 600mm, and the maximum subsidence of the roof is 277mm; Compared with the deformation of the roadway in the uncut section, the deformation is reduced by 39.6% (for both sides) and 31.8% (for the roof), effectively reducing the amount of roadway repair work and ensuring safe and efficient coal production.
Study on the height evolution and prediction of water conducting fracture zones in overlying strata during layered mining of thick coal seams
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<Abstract>(21)
Abstract:
Previous studies have focused on the height of water-conducting fracture zones in single coal seam mining, but there has been little research on predicting the height of water-conducting fracture zones in overburden rock during mining of extremely thick coal seams. This article takes the working face (9-15) of the Luanhuagou Coal Mine in the southern Xinjiang coalfield as the research area, quantitatively evaluates the development characteristics and evolution laws of the overburden rock fracture field under the condition of fully mechanized top-coal caving mining in extremely thick coal seams, and uses machine learning methods to construct a water-conducting fracture zone height prediction model based on particle swarm optimization algorithm support vector regression (PSO-SVR).Research shows that the overall evolution of fractures in the layered fully mechanized top-coal caving mining of a thick coal seam working face generally presents four stages: the rising dimension stage, the decreasing dimension stage, the stable stage, and the fluctuating stage.Among them, the fractal dimension rises rapidly due to the breakage and collapse of the roof overburden affected by mining.However, the fractal dimension of the overlying rock gradually decreases due to compaction.In addition, the correlation coefficient R of the PSO-SVR model is greater than 0.95, and the average absolute error, average deviation, and root mean square error are small. The absolute error between the model prediction value and the measured value is 12.52 m, and the relative error is 4.86%. This indicates that the PSO-SVR model can effectively and accurately predict the height of the water-conducting fracture zone in the mining of thick coal seams.
Site selection method for coal mine roadway base stations based on ray tracing path loss model
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<Abstract>(21)
Abstract:
The full coverage of 5G signals in coal mine tunnels not only improves safety, and management level, but also one of the necessary foundations for the intelligent development of coal mines. However, the narrow and multi branched structural characteristics of coal mine tunnels make it difficult to achieve comprehensive coverage of 5G signals. The high path loss in the tunnel limits wireless signal transmission, therefore, careful consideration of network planning is needed to ensure comprehensive coverage of 5G signals. This article proposes a ray tracing path loss model to determine the base station coverage radius of rectangular cross-section tunnels. And propose a discrete programming method based on geometric framework to achieve optimal coverage by minimizing the number and location optimization of base stations. To verify the effectiveness of the proposed method, a three-dimensional tunnel model was constructed and numerical simulations were conducted for validation. Under this model, using 14 base stations can achieve a coverage rate of 91.2% with an error of only 2.4%. In addition, the accuracy of the loss model was verified through measured data from coal mine tunnels. Therefore, the proposed method has practical application value for solving base station location selection in network planning.
A survey on knowledge graph construction and reasoning methods in coal mine domain
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<Abstract>(25)
Abstract:
Despite the rapid development of research and application of smart mines, coal mines’ current processes such as fully mechanized mining equipment fault diagnosis, disaster emergency rescue plan generation, disaster accident cause analysis, and production organization and operation decision-making still mainly rely on artificial experience and are not highly intelligent. An important reason for the above problems is that the current technical approach to smart mines mainly revolves around data, algorithms and computing power. Without effective use of domain expert knowledge, it is difficult to achieve autonomous decision-making. Facing the high-level construction goal of autonomous decision-making in smart mines, it is urgent to carry out research on the construction of knowledge graphs and reasoning methods in the coal mine field to form a four-element technical support system of “knowledge + data + algorithm + computing power”. Firstly, this article reviews the current research status of knowledge graphs, especially knowledge graphs in the coal mining field, and outline the development history of knowledge-driven artificial intelligence, the artificial intelligence system architecture based on knowledge graphs, the main types and representative work of knowledge graphs, and analyze the knowledge modeling situation, knowledge graph construction methods, knowledge graph usage methods and maturity of existing knowledge graphs in the coal mining field. Secondly, the challenges faced by knowledge graph construction and reasoning technology in the coal mine field are analyzed, covering aspects such as entity recognition, relationship extraction, knowledge graph fusion and error correction, and knowledge graph reasoning. Finally, the technical trends and application scenarios of knowledge graph construction and reasoning in the coal mining field are prospected. Through sorting and analysis, the following conclusions are drawn: (1)The existing knowledge graph construction goals in the coal mining field are relatively limited, the technical methods are relatively traditional, and it is difficult to support complex applications of intelligent decision-making; (2) The knowledge graph construction and reasoning technology in the coal mine field faces many challenges, including the difficulty of entity identification caused by the widespread presence of nested entities, the difficulty of relationship extraction caused by overlapping entities, the difficulty of entity alignment caused by the heterogeneity of knowledge graphs, the difficulty of correcting errors in knowledge graphs due to unclear consistency constraints on relationships between entities, and in combining knowledge graph reasoning technology with business scenarios; (3) The future application prospects of knowledge graphs in the coal mine field are broad and the demand is urgent. Therefore, it is imperative to do in-depth research on the construction and reasoning methods of knowledge graphs in this field.
Research on Coal Mine Disaster Convergence Control Platform Based on Unified Digital Base
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<Abstract>(14)
Abstract:
Design and Simulation Analysis of Adaptive Airborne Temporary Support
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<Abstract>(15)
Abstract:
During underground excavation, there is significant disturbance and the intelligence of temporary support is imbalanced, resulting in the inability of self moving temporary support to effectively control the roof based on the changes in surrounding rock generated during the mining of comprehensive excavation tunnels, which seriously affects the personal safety of underground workers. In response to the phenomenon of poor adaptability between temporary support and roof during tunnel excavation, an adaptive temporary support based on fuzzy PID control is introduced. The adaptive hydraulic control system of the support is analyzed, and the target function of fuzzy PID control is obtained by combining the three major equations of the hydraulic system. Using Simulink to establish an adaptive control system model, a MATLAB simulation platform was used to simulate the system using traditional PID control methods and fuzzy adaptive PID control. The temporary support adaptive control system was simulated and studied. After simulation experiments, it was shown that using a fuzzy adaptive PID controller system can make up for the shortcomings of conventional PID controllers. Compared with traditional PID control, fuzzy PID control is used, The adaptive control system for the support needs about 0.12 seconds to stabilize, which is reduced by 23 times, and is well adapted to the surrounding rock pressure of the roof. This proves that fuzzy PID control can effectively achieve adaptive effects on the temporary support and its pressure tracking effect is better than traditional PID control.
Research on the regulation and decision-making technology of intelligent mining working face based on the passing stroke
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<Abstract>(14)
Abstract:
Straightness control is one of the key technologies to ensure stable and efficient advancement of coal mining face, and at present, most of the wise mining face is controlled by inertial guidance to measure the travelling trajectory of the coal mining machine, but the inertial guidance system has the problems of expensive and fragile, high maintenance difficulty, which is easy to affect the normal production efficiency of coal mining. While the coal mining system installed a large number of travel, pressure and other conventional sensors and electro-hydraulic control generated by the massive detection and operation data contains the working face straightness control decision-making information, so this paper proposes the use of massive travel and other conventional data mining modelling technology, to achieve the working face straightness intelligent control decision-making. First of all, using data cleaning and smoothing filtering technology to process the hydraulic support travel data, combined with pressure data and coal mining machine position data to calculate the working face scraper conveyor propulsion distance and manual control distance, composed of straightness analysis matrix. Then use big data mining technology to analyse the straightness analysis matrix in combination with the actual production situation, clarify the minimum pushing unit of the working face, and form the straightness control decision-making feature matrix of the working face. The straightness control decision-making model is constructed by the classification algorithm of machine learning, and the accuracy rate of Random Forest algorithm is 91.41% better than other classification algorithms. The web page of the system application is developed by using internet technology and deployed in the production site for 30d normal operation, the prediction result adoption rate is 81.4%, and good results are achieved. It makes full use of the massive production data generated by the coal mining face, and at the same time reduces the labour intensity of the workers in the wise mining face.
Fault Line Selection Method for Resonant Grounding System Based on DTW- Hilbert and Improved K-means
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<Abstract>(15)
Abstract:
Due to factors such as arc suppression coils, transition resistors, and environmental noise, the fault signal characteristics of resonant grounding systems are weak, and the selection method based on a single criterion is difficult to ensure the reliability of the selection results. Therefore, this article proposes a fault line selection method for resonant grounding systems that integrates the Dynamic Time Bending Distance (DTW) algorithm and Hilbert envelope energy. Firstly, the DTW distance algorithm is used to quantitatively characterize the waveform similarity between the current sequences of each line. Secondly, to avoid potential blind spots in line selection using a single criterion, the Hilbert envelope energy is introduced to measure the amplitude of high-frequency components in transient zero sequence current signals. Thirdly, to enhance the data processing ability and efficiency of the proposed line selection method, an improved K-means clustering algorithm is used to classify the fault feature dataset. Finally, a 10kV distribution network simulation model is built in PSCAD/EMTDC to verify the feasibility and accuracy of the proposed method.
Design of miniaturized bidirectional beam antenna for mine positioning terminals
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<Abstract>(14)
Abstract:
A miniaturized bidirectional beam antenna is designed for the ultra-wide band (UWB) precise positioning of personnel in coal mines. By arranging two U-shaped monopole antennas with λ/4 spacing and introducing a U-shaped slit structure on the metal ground, the bidirectional beam radiation characteristics is realized while ensuring a compact overall structure. The overall size of the antenna is 0.3×0.3×0.01λ3. The simulation and measurement results show that the -10dB bandwidth of the antenna is 1GHz (3.6~4.6GHz), which can effectively cover the working band of the current UWB precise positioning system in coal mines(3.7~4.2GHz). The designed antenna has a good amplitude-frequency response with a peak gain of 2.2~2.5dBi during the frequency band from 3.6~4.2GHz. The proposed antenna is suitable for various UWB precise positioning terminal devices used in mining.
DITC Control Strategy for Switched Reluctance Motor Semi Direct Drive System of Belt Conveyor
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<Abstract>(7)
Abstract:
Aiming at the problems of low transmission efficiency and high failure rate in traditional asynchronous motor drive of belt conveyors, high manufacturing cost and demagnetization risk in permanent magnet motor drive, and large low-speed torque ripple in switched reluctance motor drive, a direct instantaneous torque control (DITC) strategy based on BP neural network nonlinear model is innovatively proposed to improve the control accuracy of high-power switched reluctance motor and reduce torque ripple in a certain mine of Pingdingshan coal industry group. Firstly, a 2×400kW switched reluctance semi-direct drive(SRSD) system for belt conveyors was innovatively designed, and a high-precision prediction model for the torque and magnetic flux of the switched reluctance motor was established using BP neural network. Then, combining the torque variation law of the switched reluctance motor in the commutation zone with the PWM control concept, an improved DITC control strategy is proposed, which takes torque error as the input and uses PWM to control phase current within the torque error threshold to improve the smoothness of motor operation. Finally, simulation tests were conducted on the underground transportation conditions of the belt conveyor under no-load and variable load conditions, and the following results were obtained: the improved DITC control strategy could improve the dynamic response of the switched reluctance semi direct drive system, effectively reducing torque ripple by 18.7% to 39.1% under various loads, improving the stability of belt conveyor operation, and providing reference for the application of the SRSD system on belt conveyors.
Prediction model for spontaneous combustion temperature in goaf based on GAT-Informer
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<Abstract>(16)
Abstract:
Aiming at the problem of low accuracy in predicting the spontaneous combustion temperature of coal in goaf, a GAT-Informer model based on graph attention network (GAT) and Informer model is proposed to effectively extract the spatiotemporal characteristics of coal spontaneous combustion monitoring data. Firstly, the random forest regression method and Savitzky Golay filter are used to preprocess the outliers and noise in the coal spontaneous combustion monitoring data. Secondly, based on historical monitoring data, the GAT module is used to extract spatial features between each monitoring point. Then, the Informer model is used to capture the temporal characteristics between the data. Finally, based on the fusion of spatiotemporal features, the coal temperature is predicted. The experimental results show that the coal spontaneous combustion temperature prediction model based on GAT-Informer outperforms single RNN, LSTM, GRU, and Informer prediction models on multi monitoring data. At six monitoring points, the MSE decreased by an average of 15.70%, 22.15%, 25.46%, and 36.48%, and the MAE decreased by an average of 16.00%, 14.58%, 20.29%, and 26.26%, respectively. This indicates that the GAT Informer model can effectively improve the accuracy of coal temperature prediction, prevent disasters caused by coal spontaneous combustion in goaf areas, and has important practical significance for the safety production of coal mines.
Automatic visual detection method for the abnormal state of hydraulic support at the working face
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<Abstract>(13)
Abstract:
Due to geological conditions, hydraulic power and automatic following system, abnormal state of hydraulic support generally occurs in the process of automatic movement. Relying solely on manual monitoring and relocation will seriously affect the efficiency of automatic machine following at the working face. In order to solve the above problems, the automatic visual detection method for the abnormal state of hydraulic support at the working face is proposed which captures the status of hydraulic support in real time by non-contact visual perception method Firstly, this method utilizes the YOLOv8 semantic segmentation network to achieve dynamic real-time division of the target area of the working face and accurately obtain the contour and positioning information of the hydraulic support base and push rod. By analyzing the positioning information of different hydraulic support bases and push rods, it can realize the automatic identification of hydraulic support number and extract the local image of the adjacent hydraulic support base. Finally, the improved ResNet50 convolutional classification network which fuses multi-scale feature information is used to extract features from local images. Then, the reliable automatic detection of the abnormal state is realized by combining the support number information The experimental results show that the detection accuracy of hydraulic support abnormal status is 99.17%, and the processing speed is 27.8 frames/s. At the same time, the proposed automatic detection algorithm of the abnormal state of hydraulic support is applied to the AI video surveillance system of coal face ,and meets the real-time and reliability requirements of engineering.

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